We propose a simple Track-and-Stop strategy similar to the one proposed by [1]} for the problem of Best-Arm Identification. It starts by sampling once from every arm \(a\in [K]\)  and constructs an initial estimate \(\widehat{{\mu }}_K\)  of the vector of mean rewards \({\mu }\) . After this burn-in phase, the algorithm enters a loop where at every iteration it samples the arm given by the sampling rule (REF ), updates its estimate \(\widehat{{\mu }}_t\) . Finally, the algorithm checks if the stopping rule (REF ) is satisfied, in which case it stops and returns the set of empirically \(\varepsilon \) -good arms.
